In order to solve the problem that the Total Variation (TV) iterative constraint model is easy to cause staircase artifact and cannot save the details in Computer Tomography (CT) images, an adaptive step size Non-Local Total Variation (NLTV) constraint iterative reconstruction algorithm was proposed. Considering the NLTV model is able to preserve and restore the details and textures of image, firstly, the CT model was regarded as a constraint optimization model for searching the solutions satisfying specific regular term, which means the NLTV minimization, in the solution set that satisfies the fidelity term of projection data. Then, the Algebraic Reconstruction Technique (ART) and the Split Bregman (SB) algorithm were used to ensure that the reconstructed results were constrained by the data fidelity term and regularization term. Finally, the Adaptive Steepest Descent-Projection Onto Convex Sets (ASD-POCS) algorithm was used as basic iterative framework to reconstruct images. The experimental results show that the proposed algorithm can achieve accurate results by using the projection data of 30 views under the noise-free sparse reconstruction condition. In the noise-added sparse data reconstruction experiment, the algorithm obtains the result similar to final convergence and has the Root Mean Squared Error (RMSE) as large as 2.5 times of that of ASD-POCS algorithm. The proposed algorithm can reconstruct the accurate result image under the sparse projection data and suppress the noise while improving the details reconstruction ability of TV iterative model.